干旱区生态环境知识资源中心(Arid): Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Arid

DOI	10.3390/w14132018
	Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model
	Yang, Xiazi; Maihemuti, Balati; Simayi, Zibibula; Saydi, Muattar; Na, Lu
通讯作者	Maihemuti, B
来源期刊	WATER
EISSN	2073-4441
出版年	2022
卷号	14 期号:13
英文摘要	The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization-Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins.
英文关键词	glacial hydrology glacially derived runoff PSO-LSTM model BiLSTM model
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000824561500001
WOS关键词	CLIMATE-CHANGE ; MASS-BALANCE ; SIMULATION ; RESOURCES ; MOUNTAINS ; GLACIERS ; ENERGY ; CHINA ; TESTS ; SCALE
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394807
推荐引用方式 GB/T 7714	Yang, Xiazi,Maihemuti, Balati,Simayi, Zibibula,et al. Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model[J],2022,14(13).
APA	Yang, Xiazi,Maihemuti, Balati,Simayi, Zibibula,Saydi, Muattar,&Na, Lu.(2022).Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model.WATER,14(13).
MLA	Yang, Xiazi,et al."Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model".WATER 14.13(2022).